A manufacturing bottleneck case study trough the theory of constraints and computational simulation of the proposed bottleneck solution

Barbosa, Gustavo Lemes Leite; Ordóñez, Robert Eduardo Cooper; Anholon, Rosley; Sánchez, Mauricio Andrés Rodríguez

doi:10.14488/bjopm.2018.v15.n1.a6

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…There are different abstraction levels of simulation models and they depend on the detailing of the built model [11]. The higher the level of approximation between the simulation model and the real model, the greater the complexity and necessity for detail.…”

Section: Simulation and Optimization In Textile Industrymentioning

confidence: 99%

An Optimization Tool for Production Planning: A Case Study in a Textile Industry

et al. 2021

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The textile industry is an important sector of the Brazilian economy, being considered the fifth largest textile industry in the world. To support further growth and development in this sector, this document proposes a process for production analysis through the use of Discrete Event Simulation (DES) and optimization through genetic algorithms. The focus is on production planning for weaving processes and optimization to help make decisions about batch sizing and production scheduling activities. In addition, the correlations between some current technological trends and their implications for the textile industry are also highlighted. Another important contribution of this study is to detail the use of the commercial software Tecnomatix Plant Simulation 13®, to simulate and optimize a production problem by applying genetic algorithms with real production data.

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Section: Simulation and Optimization In Textile Industrymentioning

confidence: 99%

An Optimization Tool for Production Planning: A Case Study in a Textile Industry

et al. 2021

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Research on dynamic bottleneck prediction methods in manufacturing workshops

Xia,

Lin,

Han

2024

J. Phys.: Conf. Ser.

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To address the challenge of identifying bottleneck responsibilities when multiple bottlenecks arise in process workshops and predicting bottleneck trends before their occurrence, a novel dynamic prediction method based on the Markov chain is introduced. This approach effectively identifies all bottlenecks in a multi-bottleneck process shop and identifies the primary bottleneck unit with the highest level of responsibility, enabling anticipation of bottleneck shifts prior to their occurrence. Initially, the concept of bottleneck is elaborated, accounting for the intricate interplay among manufacturing processes, leading to the development of a static mathematical model for multi-bottleneck prediction incorporating parameters such as time, cost, and quality. This model evaluates the bottleneck severity of each manufacturing unit, facilitating the identification of all bottleneck units and the assignment of bottleneck responsibilities, thereby establishing primary and secondary bottlenecks. Subsequently, a bottleneck state transition matrix is formed based on historical bottleneck processes, initializing the state matrix through the process with the highest bottleneck responsibility in the static setting. Leveraging the Markov chain prediction technique, the method anticipates the location of bottleneck transitions preceding the occurrence of bottleneck drift. Finally, simulations were conducted in an authentic production line environment to validate the method’s efficacy. Results indicate a 9.1% increase in the main bottleneck coincidence rate compared to single-bottleneck prediction models. Moreover, the Markov chain prediction accurately forecasts bottleneck trends prior to drift occurrences, boasting an 86.4% accuracy rate. These findings underscore the applicability and guidance potential of the proposed method.

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A manufacturing bottleneck case study trough the theory of constraints and computational simulation of the proposed bottleneck solution

Cited by 2 publications

References 4 publications

An Optimization Tool for Production Planning: A Case Study in a Textile Industry

An Optimization Tool for Production Planning: A Case Study in a Textile Industry

Research on dynamic bottleneck prediction methods in manufacturing workshops

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